An analog neural network for linear programming: analysis, design and simulation

Presents an analog recurrent neural network for solving linear programs. The proposed analog neural network is asymptotically stable and able to generate optimal solutions to linear programming problems. The asymptotic properties of the proposed analog neural network for linear programming are analyzed theoretically. The circuit design for realizing the analog network is discussed. Two illustrative examples are also presented to demonstrate the performance and operating characteristics of the analog neural network